Application of adaptive network based fuzzy inference system for model reconstruction in reverse engineering

被引:0
|
作者
Ma Zi [1 ]
Xu Huipu [1 ]
机构
[1] Dalian Maritime Univ, Automat Res Ctr, Dalian 116026, Peoples R China
来源
PROCEEDINGS OF THE 24TH CHINESE CONTROL CONFERENCE, VOLS 1 AND 2 | 2005年
关键词
adaptive network; Fuzzy Inference System; reverse engineering;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Combining the both of technologies, fuzzy neural network and laser Surface data measurement., a novel model reconstruction methodology is presented. This model reconstruction scheme includes two main parts, One is Surface data measurement system, and the other one is model reconstruction algorithm. The surface data measurement system consists of a vision system with a smart laser camera and a PC computer, the system is developed to measure data for freeform Surface with complex shape. Using an Adaptive Network based Fuzzy Inference System (ANFIS), the model reconstruction algorithm is designed. For demonstrating the effectiveness of the presented scheme, the surface data of ail existing part are measured, the point cloud data with good accuracy are used to train the ANFIS so that the network model is obtained. From comparing the output of network model data with the sample data, it can be found that the trained network model can match the real surface very well.
引用
收藏
页码:1077 / 1081
页数:5
相关论文
共 50 条
  • [1] The use of adaptive network-based fuzzy inference system for marine ahrs
    Li Q.
    Sun F.
    Yu F.
    Gao W.
    Gyroscopy and Navigation, 1600, Maik Nauka Publishing / Springer SBM (05): : 108 - 112
  • [2] Application of an Adaptive Neural-Based Fuzzy Inference System Model for Predicting Leaf Area
    Amiri, Mohammad Javad
    Shabani, Ali
    COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS, 2017, 48 (14) : 1669 - 1683
  • [3] Robot vision system and artificial neural network for model reconstruction in reverse engineering
    Ma, Zi
    Xu, Huipu
    Hu, Ying
    Huang, Jin
    Dong, Hu
    WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, : 139 - 139
  • [4] Power system stabilizer based on a self-learning adaptive network fuzzy inference system
    Malik, OP
    Hariri, A
    TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2002, 24 (02) : 153 - 173
  • [5] Application of Adaptive Neuro-Fuzzy Inference System for Physical Habitat Simulation
    Zhao, Yue
    Zhou, Jianzhong
    Bi, Sheng
    Zhang, Huajie
    2013 10TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD), 2013, : 349 - 353
  • [6] An Adaptive Network Fuzzy Inference System Approach for Site investigation
    Jelusic, Primoz
    Zlender, Bojan
    GEOTECHNICAL TESTING JOURNAL, 2014, 37 (03):
  • [7] CHOQUET INTEGRAL-OWA BASED ADAPTIVE NEURAL FUZZY INFERENCE SYSTEM WITH APPLICATION
    Chai Yuanyuan
    Jia Limin
    INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS, 2011, 10 (01) : 15 - 34
  • [8] Particles flow identification in pipeline using adaptive network-based fuzzy inference system and electrodynamic sensors
    Khairalla, M.
    Rahmat, M. E.
    Wahab, N. Abdul
    Thuku, I. T.
    Tajdari, T.
    Yusuf, Abdulrahman Amuda
    SENSOR REVIEW, 2014, 34 (02) : 201 - 208
  • [9] Adaptive network-based fuzzy inference system short-term load forecasting
    Saha A.K.
    Chowdhury S.
    Chowdhury S.
    Domijan A.
    International Journal of Power and Energy Systems, 2011, 31 (03) : 154 - 161
  • [10] A Model Fuzzy Inference System for Online Social Network Analysis
    Raj, Ebin Deni
    Babu, L. D. Dhinesh
    2015 INTERNATIONAL CONFERENCE ON COMPUTING AND NETWORK COMMUNICATIONS (COCONET), 2015, : 582 - 588